Life’s a Gas: A Thermodynamic Theory of Biological Evolution

Skene, Keith R.

doi:10.3390/e17085522

Cited by 52 publications

(35 citation statements)

References 100 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study was conducted at the Joseph W. Jones Ecological Research Center in southwestern Georgia, USA (31.2201 • N, 84.4792 • W), from January 2009 to December 2016. The three sites are maintained by frequent low-intensity fire on a 2-year return interval and were last burned in 2015 (Starr et al, 2016). The climate is humid subtropical with a mean annual precipitation of 1310 mm (Kirkman et al, 2001).…”

Section: Site Descriptionmentioning

confidence: 99%

“…Barometric pressure data were used to correct fluxes to standard atmospheric pressure. Flux data screening was applied to eliminate 30 min fluxes of NEE, H and LE, resulting from systematic errors as described in Whelan et al (2013) and Starr et al (2016). Such errors encompassed (amongst other things) rain, poor coupling of the canopy and the atmosphere (defined by the friction velocity, ustar), and excessive variation from half-hourly means.…”

Section: Data Processingmentioning

confidence: 99%

“…Missing half-hourly data were gap-filled as described in Whelan et al (2013) and Starr et al (2016). Daytime and nighttime data were estimated utilizing a Michaelis-Menten approach for (PAR > 10 µmol m −2 s −1 ) and a modification of the Lloyd and Taylor (1994) model (PAR ≤ 10 µmol m −2 s −1 ), respectively.…”

Section: Data Processingmentioning

confidence: 99%

See 2 more Smart Citations

Quantifying energy use efficiency via entropy production: a case study from longleaf pine ecosystems

Wiesner¹,

Staudhammer²,

Stoy³

et al. 2019

Biogeosciences

View full text Add to dashboard Cite

Abstract. Ecosystems are open systems that exchange matter and energy with their environment. They differ in their efficiency in doing so as a result of their location on Earth, structure and disturbance, including anthropogenic legacy. Entropy has been proposed to be an effective metric to describe these differences as it relates energy use efficiencies of ecosystems to their thermodynamic environment (i.e., temperature) but has rarely been studied to understand how ecosystems with different disturbance legacies respond when confronted with environmental variability. We studied three sites in a longleaf pine ecosystem with varying levels of anthropogenic legacy and plant functional diversity, all of which were exposed to extreme drought. We quantified radiative (effrad), metabolic and overall entropy changes – as well as changes in exported to imported entropy (effflux) in response to drought disturbance and environmental variability using 24 total years of eddy covariance data (8 years per site). We show that structural and functional characteristics contribute to differences in energy use efficiencies at the three study sites. Our results demonstrate that ecosystem function during drought is modulated by decreased absorbed solar energy and variation in the partitioning of energy and entropy exports owing to differences in site enhanced vegetation index and/or soil water content. Low effrad and metabolic entropy as well as slow adjustment of effflux at the anthropogenically altered site prolonged its recovery from drought by approximately 1 year. In contrast, stands with greater plant functional diversity (i.e., the ones that included both C3 and C4 species) adjusted their entropy exports when faced with drought, which accelerated their recovery. Our study provides a path forward for using entropy to determine ecosystem function across different global ecosystems.

show abstract

Section: Site Descriptionmentioning

confidence: 99%

Section: Data Processingmentioning

confidence: 99%

See 1 more Smart Citation

Quantifying energy use efficiency via entropy production: a case study from longleaf pine ecosystems

Wiesner¹,

Staudhammer²,

Stoy³

et al. 2019

Biogeosciences

View full text Add to dashboard Cite

show abstract

“…The MEP principle has been applied to both abiotic and biotic processes (Kleidon and Lorenz, 2005;Kleidon et al, 2010;Dewar et al, 2014b), and we have used MEP to model periodically forced methanotrophic microbial communities (Vallino et al, 2014) and investigate metabolic switching in nitrate reducing environments (Algar and Vallino, 2014). While MEP is consistent with Darwinian evolution, and likely guides its trajectory (Goldenfeld and Woese, 2011;Skene, 2015Skene, , 2017Judson, 2017), it has yet to gain general acceptance in theoretical or experimental ecological communities largely because of an insufficient number of case studies and uncertainty in how to apply it.…”

Section: Introductionmentioning

confidence: 99%

Using Maximum Entropy Production to Describe Microbial Biogeochemistry Over Time and Space in a Meromictic Pond

Vallino

Huber

2018

Front. Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…Then the average of the log fitnesses is Average fitness = ∑ p p log p /p = −KL where KL is the classic expression for relative entropy (Kullback-Liebler) of the adult array of types relative to the initial newborn array [13]. This calculation provides immediate access to the maximum entropy production approach that is widely used throughout science for exploiting hypotheses about fundamental processes (e.g., inheritance mode and dispersal) to create forecasts of measurable patterns, including ecological adaptation and assemblages [51][52][53][54][55] (although some of those are not based on the four fundamental processes outlined above [51]). Analysis of adaptation might also exploit the similarity of Kullback-Liebler to logit methods already used for analysis of adaptation [5].…”

Section: Notmentioning

confidence: 99%

Entropy, or Information, Unifies Ecology and Evolution and Beyond

Sherwin

2018

Entropy

View full text Add to dashboard Cite

This article discusses how entropy/information methods are well-suited to analyzing and forecasting the four processes of innovation, transmission, movement, and adaptation, which are the common basis to ecology and evolution. Macroecologists study assemblages of differing species, whereas micro-evolutionary biologists study variants of heritable information within species, such as DNA and epigenetic modifications. These two different modes of variation are both driven by the same four basic processes, but approaches to these processes sometimes differ considerably. For example, macroecology often documents patterns without modeling underlying processes, with some notable exceptions. On the other hand, evolutionary biologists have a long history of deriving and testing mathematical genetic forecasts, previously focusing on entropies such as heterozygosity. Macroecology calls this Gini-Simpson, and has borrowed the genetic predictions, but sometimes this measure has shortcomings. Therefore it is important to note that predictive equations have now been derived for molecular diversity based on Shannon entropy and mutual information. As a result, we can now forecast all major types of entropy/information, creating a general predictive approach for the four basic processes in ecology and evolution. Additionally, the use of these methods will allow seamless integration with other studies such as the physical environment, and may even extend to assisting with evolutionary algorithms.These alternatives or 'alleles' can be characterized by the probabilities P(T) and P(A) in the biological population (usually at any position in the DNA, called a 'SNP' or single nucleotide polymorphism, only two of the possible four nucleotides are found). These molecular variants are exactly analogous to alternative species in ecological assemblages, and in most cases, measures or forecasts made in one of these areas have been, or could be, transferred directly to the other.This article will discuss how entropy/information methods are well suited to analyzing and forecasting the four common processes of innovation, transmission, movement, and adaptation in ecology and evolution. Despite the focus on a few variant types, this will apply broadly to variants of all types: DNA, epigenetics, behavior, species, the physical environment, etc., as well as their interactions [2,3]. Background: Measuring Biological Entropy, Information and DiversityMeasurement of ecological or evolutionary variants uses various entropy or information measures (Table 1a). The measures are all part of a 'q-profile' derived from a general power-sum of variant proportions (0 ≤ q ≤ ∞) [4,5]), composed of q D measures on a common scale of the 'effective' number of variants, which means the number of equally-frequent variants that would give the same entropy ( q H) as the typically unequal array of variants in the sampled system. The use of the q D profile has been recommended because each q value emphasizes different aspects of the diversity [6,7]; for example, higher q ...

show abstract

Life’s a Gas: A Thermodynamic Theory of Biological Evolution

Cited by 52 publications

References 100 publications

Quantifying energy use efficiency via entropy production: a case study from longleaf pine ecosystems

Quantifying energy use efficiency via entropy production: a case study from longleaf pine ecosystems

Using Maximum Entropy Production to Describe Microbial Biogeochemistry Over Time and Space in a Meromictic Pond

Entropy, or Information, Unifies Ecology and Evolution and Beyond

Contact Info

Product

Resources

About